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María de los Ángeles Carrasco-Ruiz,Laura G. Hernández-Aragón,Jesús Ramsés Chávez-Ríos,Jorge Rodríguez-Antolín,Pablo Pacheco,Margarita Martínez-Gómez,Estela Cuevas-Romero,Francisco Castelán 대한배뇨장애요실금학회 2018 International Neurourology Journal Vol.22 No.3
Purpose: To characterize the relationship between serum estradiol levels and the expression of glucose transporter type 4 (Glut4) in the pubococcygeus and iliococcygeus muscles in female rats. Methods: The muscles were excised from virgin rats during the metestrus and proestrus stages of the estrous cycle, and from sham and ovariectomized rats implanted with empty or estradiol benzoate–filled capsules. The expression of estrogen receptors (ERs) was inspected in the muscles at metestrus and proestrus. Relative Glut4 expression, glycogen content, and serum glucose levels were measured. Appropriate statistical tests were done to identify significant differences (P≤0.05). Results: The pubococcygeus and iliococcygeus muscles expressed ERα and ERβ. Glut4 expression and glycogen content in the pubococcygeus muscle were higher at proestrus than at metestrus. No significant changes were observed in the iliococcygeus muscle. In ovariectomized rats, the administration of estradiol benzoate increased Glut4 expression and glycogen content in the pubococcygeus muscle alone. Conclusions: High serum estradiol levels increased Glut4 expression and glycogen content in the pubococcygeus muscle, but not in the iliococcygeus muscle.
Verónica García-Villamar,Laura G. Hernández-Aragón,Jesús R. Chávez-Ríos,Arturo Ortega,Margarita Martínez-Gómez,Francisco Castelán 대한배뇨장애요실금학회 2018 International Neurourology Journal Vol.22 No.S2
Purpose: To evaluate the expression of glial cell line-derived neurotrophic factor (GDNF) and its receptor, GDNF family receptor alpha subunit 1 (GFRα-1) in the pelvic (middle third) vagina and, particularly, in the paravaginal ganglia of nulliparous and primiparous rabbits. Methods: Chinchilla-breed female rabbits were used. Primiparas were killed on postpartum day 3 and nulliparas upon reaching a similar age. The vaginal tracts were processed for histological analyses or frozen for Western blot assays. We measured the ganglionic area, the Abercrombie-corrected number of paravaginal neurons, the cross-sectional area of the neuronal somata, and the number of satellite glial cells (SGCs) per neuron. The relative expression of both GDNF and GFRα-1 were assessed by Western blotting, and the immunostaining was semiquantitated. Unpaired two-tailed Student t -test or Wilcoxon test was used to identify statistically significant differences (P≤0.05) between the groups. Results: Our findings demonstrated that the ganglionic area, neuronal soma size, Abercrombie-corrected number of neurons, and number of SGCs per neuron were similar in nulliparas and primiparas. The relative expression of both GDNF and GFRα- 1 was similar. Immunostaining for both GDNF and GFRα-1 was observed in several vaginal layers, and no differences were detected regarding GDNF and GFRα-1 immunostaining between the 2 groups. In the paravaginal ganglia, the expression of GDNF was increased in neurons, while that of GFRα-1 was augmented in the SGCs of primiparous rabbits. Conclusions: The present findings suggest an ongoing regenerative process related to the recovery of neuronal soma size in the paravaginal ganglia, in which GDNF and GFRα-1 could be involved in cross-talk between neurons and SGCs.
José D. Martínez-Morales,Elvia R. Palacios-Hernández,Gerardo A. Velázquez-Carrillo 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.6
This paper proposes a hybrid learning of artificial neural network (ANN) with the nondominated sorting genetic algorithm-II (NSGAII)to improve accuracy in order to predict the exhaust emissions of a four stroke spark ignition (SI) engine. In the proposed approach, thegenetic algorithm (GA) determines initial weights of local linear model tree (LOLIMOT) neural networks. A multi-objective optimizationproblem is determined. A sensitivity analysis is performed on NSGA-II parameters in order to provide better solutions along theoptimal Pareto front. Then, a fuzzy decision maker and the technique for order preference by similarity to ideal solution (TOPSIS) areemployed to select compromised solutions among the obtained Pareto solutions. The LOLIMOT-GA responses are compared with theprovided by radial basis function (RBF) and multilayer perceptron (MLP) neural networks in terms of correlation coefficient R².
MODELING ENGINE FUEL CONSUMPTION AND NOx WITH RBF NEURAL NETWORK AND MOPSO ALGORITHM
J. D. MARTÍNEZ-MORALES,E. R. PALACIOS-HERNÁNDEZ,G. A. VELÁZQUEZ-CARRILLO 한국자동차공학회 2015 International journal of automotive technology Vol.16 No.6
In this study, artificial neural network (ANN) modeling is used to predict the fuel consumption and NOx emission of a four stroke spark ignition (SI) engine. Calibration engineers frequently want to know the responses of an engine for the entire range of operating conditions in order to change engine control parameters in the electronic control unit (ECU), to improve performance and reduce emissions. However, testing the engine for the complete range of operating conditions is a very time and labor consuming task. As alternative, ANN is used in order to predict fuel consumption and NOx emission. In the proposed approach, the multi-objective particle swarm optimization (MOPSO) is used to determine weights of radial basis function (RBF) neural networks. The goal is to minimize performance criteria as root mean square error (RMSE) and model complexity. A sensitivity analysis is performed on MOPSO parameters in order to provide better solutions along the optimal Pareto front. In order to select a compromised solution among the obtained Pareto solutions, a fuzzy decision maker is employed. The correlation coefficient R2 is used to compare the engine responses with the obtained by the proposed approach.